Evaluation of obstacle modelling approaches for resource assessment and small wind turbine siting: case study in the northern Netherlands

Abstract

Abstract. Growth in adoption of distributed wind turbines for energy generation is significantly impacted by challenges associated with siting and accurate estimation of the wind resource. Small turbines, at hub heights of 40 m or less, are greatly impacted by terrestrial obstacles such as built structures and vegetation that can cause complex wake effects. While some progress in high-fidelity complex fluid dynamics (CFD) models has increased the potential accuracy for modelling the impacts of obstacles on turbulent wind flow, these models are too computationally expensive for practical siting and resource assessment applications. To understand the efficacy of available models in situ, this study evaluates classic and commonly used methods alongside new state-of-the-art lower-order models derived from CFD simulations and machine learning approaches. This evaluation is conducted using a subset of an extensive original dataset of measurements from more than 300 operational wind turbines in the northern Netherlands. The results show that data-driven methods (e.g. machine learning and statistical modelling) are most effective at predicting production at real sites with an average error in annual energy production of 2.5 %. When sufficient data may not be available de novo to support these data-driven approaches, models derived from high-fidelity simulations show promise and reliably outperform classic methods. On average these models have 6.3 %–11.5 % error compared with 26 % for classic methods and 27 % baseline error for reanalysis data without obstacle correction. While more performant on average, these methods are also sensitive to the quality of obstacle descriptions and reanalysis inputs.

Resource Assessment for Distributed Wind Energy: An Evaluation of Best-Practice Methods in the Continental US

“…While we have evaluated the inclusion of meteorological data from diverse sites (including airports), we believe these data to be best-in-class, both for broad spatial distribution and consistency in instrument and measurement quality. We follow the same method of multivariate least squares linear bias correction proposed and evaluated in our prior work in the Northern Netherlands [5]. This involves fitting a multiple linear regression with the following form (Equation 1): Where 𝑤 !…”

“…This involves fitting a multiple linear regression with the following form (Equation 1): Where 𝑤 ! "# is the observed wind speed at the meteorological tower, 𝑤 %&' is the WTK estimate for the wind speed, 𝑑 %&' is the direction of the model data in degrees, h is the hour of the day (0-23), and m is the month of the year (1)(2)(3)(4)(5)(6)(7)(8)(9)(10)(11)(12). Values for the coefficients x0, x1, x2, x3, and x4 are fitted with least squares regression.…”

“…Models determined to be usable for this application include: (1) classic simplified empirical models well utilized by industry (e.g., [2]), (2) new reduced-order models developed from machine learning and complex fluid dynamics (CFD) simulations [3], and (3) urban dispersion models adapted for use in wind turbine siting [4]. A prior study with EAZ Wind in the Northern Netherlands found newer obstacle models tended to outperform legacy methods and that data-informed approaches, including a priori bias correction, resulted in the largest performance gains [5]. The present study significantly extends that work to develop best practices and recommendations in a broad range of topographies, environments, and resource conditions in the continental US.…”